Moving Matters: The Causal Effect of Moving Schools on Student Performance
Bibliographic record
Abstract
Policy makers and analysts often view the reduction of student mobility across schools as a way to improve academic performance. Prior work indicates that children do worse in the year of a school move, but has been largely unsuccessful in isolating the causal effects of mobility. We use longitudinal data on students in New York City public elementary and middle schools to isolate the causal effects of school moves on student performance. We account for observed and time-invariant differences between movers and non-movers using rich data on student sociodemographic and education program characteristics and student fixed effects. To address the potential endogeneity of school moves arising from unobserved, time-varying factors, we use three sets of plausibly exogenous instruments for mobility: first-grade school grade span, grade span of zoned middle school, and building sale. We find that in the medium term, students making structural moves perform significantly worse in both English language arts (ELA) and math, whereas those making nonstructural moves experience a significant increase in ELA performance. In the short term, there is an additional negative effect for structural moves in ELA. These effects are meaningful in magnitude and results are robust to a variety of alternative specifications, instruments, and samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".